Numbers reported in text - para 3
# effect of ParC when added to GyrA-83 background (absence of any genes or GyrA-87)
# MIC data
QRDR_MIC_GyrA83background_noGenes_dat <- cipro_antibiogram %>%
filter(aac6==0 & acquired_genes==0 & `GyrA-83`==1 & `GyrA-87`==0) %>%
filter(grepl('MIC.*$', Laboratory.Typing.Method)) %>%
select(Measurement, Resistance.phenotype, resistant, nonWT_binary, ParC_mutations, `ParC-80`, `ParC-84`)
wilcox.test(log2(as.numeric(Measurement)) ~ ParC_mutations, data=QRDR_MIC_GyrA83background_noGenes_dat)
##
## Wilcoxon rank sum test with continuity correction
##
## data: log2(as.numeric(Measurement)) by ParC_mutations
## W = 13226, p-value = 0.001341
## alternative hypothesis: true location shift is not equal to 0
summary(lm(log2(as.numeric(Measurement)) ~ ParC_mutations, data=QRDR_MIC_GyrA83background_noGenes_dat))
##
## Call:
## lm(formula = log2(as.numeric(Measurement)) ~ ParC_mutations,
## data = QRDR_MIC_GyrA83background_noGenes_dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.7069 -0.0860 -0.0860 0.1036 7.9140
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.7069 0.1405 5.033 6.27e-07 ***
## ParC_mutations 0.3791 0.1472 2.576 0.0102 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.07 on 649 degrees of freedom
## Multiple R-squared: 0.01012, Adjusted R-squared: 0.008596
## F-statistic: 6.636 on 1 and 649 DF, p-value: 0.01021
summary(as.numeric(QRDR_MIC_GyrA83background_noGenes_dat$Measurement)[QRDR_MIC_GyrA83background_noGenes_dat$`ParC_mutations`==1])
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.250 2.000 2.000 4.674 2.000 512.000
summary(as.numeric(QRDR_MIC_GyrA83background_noGenes_dat$Measurement)[QRDR_MIC_GyrA83background_noGenes_dat$`ParC_mutations`==0])
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.125 1.000 1.000 2.894 4.000 16.000
# disk diffusion
QRDR_DD_GyrA83background_noGenes_dat <- cipro_antibiogram %>%
filter(aac6==0 & acquired_genes==0 & `GyrA-83`==1 & `GyrA-87`==0) %>%
filter(Laboratory.Typing.Method=="Disk diffusion") %>%
select(Measurement, Resistance.phenotype, resistant, nonWT_binary, ParC_mutations, `ParC-80`, `ParC-84`)
wilcox.test(as.numeric(Measurement) ~ ParC_mutations, data=QRDR_DD_GyrA83background_noGenes_dat)
##
## Wilcoxon rank sum test with continuity correction
##
## data: as.numeric(Measurement) by ParC_mutations
## W = 1112.5, p-value = 5.475e-09
## alternative hypothesis: true location shift is not equal to 0
summary(lm(as.numeric(Measurement) ~ ParC_mutations, data=QRDR_DD_GyrA83background_noGenes_dat))
##
## Call:
## lm(formula = as.numeric(Measurement) ~ ParC_mutations, data = QRDR_DD_GyrA83background_noGenes_dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.870 -3.585 -1.727 3.202 20.415
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 18.870 1.006 18.76 < 2e-16 ***
## ParC_mutations -9.285 1.204 -7.71 4.58e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.823 on 74 degrees of freedom
## Multiple R-squared: 0.4454, Adjusted R-squared: 0.4379
## F-statistic: 59.44 on 1 and 74 DF, p-value: 4.582e-11
summary(as.numeric(QRDR_DD_GyrA83background_noGenes_dat$Measurement)[QRDR_DD_GyrA83background_noGenes_dat$ParC_mutations==1])
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 6.000 6.000 6.000 9.585 13.000 30.000
summary(as.numeric(QRDR_DD_GyrA83background_noGenes_dat$Measurement)[QRDR_DD_GyrA83background_noGenes_dat$ParC_mutations==0])
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 11.00 16.00 20.00 18.87 22.00 25.00
# test categorial phenotypes
# all genomes with no acquired genes (MIC/DD)
QRDR_noGenes_dat <- cipro_antibiogram %>%
filter(aac6==0 & acquired_genes==0) %>%
select(Measurement, Resistance.phenotype, resistant, nonWT_binary, QRDR_mutations) %>%
mutate(qrdr_bin=if_else(QRDR_mutations==0, 0, 1))
# nonWT vs presence/absence
fisher.test(table(QRDR_noGenes_dat$nonWT_binary, QRDR_noGenes_dat$qrdr_bin))
##
## Fisher's Exact Test for Count Data
##
## data: table(QRDR_noGenes_dat$nonWT_binary, QRDR_noGenes_dat$qrdr_bin)
## p-value < 2.2e-16
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 447.8269 1010.4418
## sample estimates:
## odds ratio
## 667.2061
# resistance vs presence/absence
fisher.test(table(QRDR_noGenes_dat$resistant, QRDR_noGenes_dat$qrdr_bin))
##
## Fisher's Exact Test for Count Data
##
## data: table(QRDR_noGenes_dat$resistant, QRDR_noGenes_dat$qrdr_bin)
## p-value < 2.2e-16
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 956.5967 1777.6825
## sample estimates:
## odds ratio
## 1299.855
Numbers reported in text - para 4
effect of presence/absence of any QRDR, in absence of any acquired
genes
# total strains without PMQR or aac6
nrow(QRDR_noGenes_dat)
## [1] 7452
# presence of QRDR vs MIC
QRDR_MIC_noGenes_dat <- cipro_antibiogram %>%
filter(grepl('MIC.*$', Laboratory.Typing.Method)) %>%
filter(aac6==0 & acquired_genes==0) %>%
select(Measurement, Resistance.phenotype, resistant, nonWT_binary, QRDR_mutations, `GyrA-83`, `ParC-80`) %>%
mutate(qrdr_bin=if_else(QRDR_mutations==0, 0, 1))
wilcox.test(log2(as.numeric(Measurement)) ~ qrdr_bin, data=QRDR_MIC_noGenes_dat)
##
## Wilcoxon rank sum test with continuity correction
##
## data: log2(as.numeric(Measurement)) by qrdr_bin
## W = 111846, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0
summary(lm(log2(as.numeric(Measurement)) ~ qrdr_bin, data=QRDR_MIC_noGenes_dat))
##
## Call:
## lm(formula = log2(as.numeric(Measurement)) ~ qrdr_bin, data = QRDR_MIC_noGenes_dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.1708 -0.1708 -0.0558 -0.0558 7.9442
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.94422 0.01661 -117.1 <2e-16 ***
## qrdr_bin 3.11501 0.02830 110.1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9326 on 4809 degrees of freedom
## Multiple R-squared: 0.7159, Adjusted R-squared: 0.7158
## F-statistic: 1.212e+04 on 1 and 4809 DF, p-value: < 2.2e-16
summary(as.numeric(QRDR_MIC_noGenes_dat$Measurement)[QRDR_MIC_noGenes_dat$qrdr_bin==1])
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.125 2.000 2.000 5.525 2.000 512.000
summary(as.numeric(QRDR_MIC_noGenes_dat$Measurement)[QRDR_MIC_noGenes_dat$qrdr_bin==0])
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0300 0.2500 0.2500 0.3464 0.2500 64.0000
# presence of QRDR vs DD zone
QRDR_DD_noGenes_dat <- cipro_antibiogram %>%
filter(aac6==0 & acquired_genes==0) %>%
filter(Laboratory.Typing.Method=="Disk diffusion") %>%
select(Measurement, Resistance.phenotype, resistant, nonWT_binary, QRDR_mutations) %>%
mutate(qrdr_bin=if_else(QRDR_mutations==0, 0, 1))
wilcox.test(as.numeric(Measurement) ~ qrdr_bin, data=QRDR_DD_noGenes_dat)
##
## Wilcoxon rank sum test with continuity correction
##
## data: as.numeric(Measurement) by qrdr_bin
## W = 320646, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0
summary(lm(as.numeric(Measurement) ~ qrdr_bin, data=QRDR_DD_noGenes_dat))
##
## Call:
## lm(formula = as.numeric(Measurement) ~ qrdr_bin, data = QRDR_DD_noGenes_dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -22.6659 -1.6659 0.3341 1.3341 18.1154
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 28.66587 0.06349 451.51 <2e-16 ***
## qrdr_bin -16.78125 0.28616 -58.64 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.181 on 2639 degrees of freedom
## Multiple R-squared: 0.5658, Adjusted R-squared: 0.5656
## F-statistic: 3439 on 1 and 2639 DF, p-value: < 2.2e-16
summary(as.numeric(QRDR_DD_noGenes_dat$Measurement)[QRDR_DD_noGenes_dat$qrdr_bin==1])
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 6.00 6.00 9.00 11.88 17.00 30.00
summary(as.numeric(QRDR_DD_noGenes_dat$Measurement)[QRDR_DD_noGenes_dat$qrdr_bin==0])
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 6.00 27.00 29.00 28.67 30.00 44.00
# presence of QRDR vs nonWT
fisher.test(table(QRDR_noGenes_dat$nonWT_binary, QRDR_noGenes_dat$qrdr_bin))
##
## Fisher's Exact Test for Count Data
##
## data: table(QRDR_noGenes_dat$nonWT_binary, QRDR_noGenes_dat$qrdr_bin)
## p-value < 2.2e-16
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 447.8269 1010.4418
## sample estimates:
## odds ratio
## 667.2061
# presence of QRDR vs R
fisher.test(table(QRDR_noGenes_dat$resistant, QRDR_noGenes_dat$qrdr_bin))
##
## Fisher's Exact Test for Count Data
##
## data: table(QRDR_noGenes_dat$resistant, QRDR_noGenes_dat$qrdr_bin)
## p-value < 2.2e-16
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 956.5967 1777.6825
## sample estimates:
## odds ratio
## 1299.855
effect of number of QRDR mutations, in absence of any acquired
genes
# MIC vs QRDR count
QRDR_MIC_noGenes_dat <- QRDR_MIC_noGenes_dat %>%
mutate(QRDR_0_1_2 = if_else(QRDR_mutations>2, 2, QRDR_mutations)) %>%
mutate(QRDR_0_1_2_3 = if_else(QRDR_mutations>3, 3, QRDR_mutations)) %>%
mutate(QRDR_1_2 = if_else(QRDR_0_1_2==0, NA, QRDR_0_1_2)) %>%
mutate(QRDR_2_3 = if_else(QRDR_0_1_2<2, NA, QRDR_0_1_2_3))
# median MICs, grouped by QRDR count
summary(as.numeric(QRDR_MIC_noGenes_dat$Measurement)[QRDR_MIC_noGenes_dat$QRDR_mutations==0])
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0300 0.2500 0.2500 0.3464 0.2500 64.0000
summary(as.numeric(QRDR_MIC_noGenes_dat$Measurement)[QRDR_MIC_noGenes_dat$QRDR_mutations==1])
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.125 1.000 1.000 2.592 2.000 16.000
summary(as.numeric(QRDR_MIC_noGenes_dat$Measurement)[QRDR_MIC_noGenes_dat$QRDR_mutations>2])
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.125 2.000 2.000 6.247 2.000 512.000
# test for difference in MIC with QRDR count
summary(lm(log2(as.numeric(Measurement)) ~ factor(QRDR_0_1_2), data=QRDR_MIC_noGenes_dat))
##
## Call:
## lm(formula = log2(as.numeric(Measurement)) ~ factor(QRDR_0_1_2),
## data = QRDR_MIC_noGenes_dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.1987 -0.1987 -0.0558 -0.0558 7.9442
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.94422 0.01655 -117.47 <2e-16 ***
## factor(QRDR_0_1_2)1 2.49977 0.11079 22.56 <2e-16 ***
## factor(QRDR_0_1_2)2 3.14296 0.02862 109.82 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9295 on 4808 degrees of freedom
## Multiple R-squared: 0.7178, Adjusted R-squared: 0.7177
## F-statistic: 6116 on 2 and 4808 DF, p-value: < 2.2e-16
summary(lm(log2(as.numeric(Measurement)) ~ QRDR_mutations, data=QRDR_MIC_noGenes_dat))
##
## Call:
## lm(formula = log2(as.numeric(Measurement)) ~ QRDR_mutations,
## data = QRDR_MIC_noGenes_dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.5645 -0.1174 -0.1174 -0.1174 8.5845
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.88258 0.01725 -109.1 <2e-16 ***
## QRDR_mutations 1.14904 0.01122 102.4 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9811 on 4809 degrees of freedom
## Multiple R-squared: 0.6856, Adjusted R-squared: 0.6855
## F-statistic: 1.049e+04 on 1 and 4809 DF, p-value: < 2.2e-16
# DD vs QRDR count
QRDR_DD_noGenes_dat <- QRDR_DD_noGenes_dat %>%
mutate(QRDR_0_1_2 = if_else(QRDR_mutations>2, 2, QRDR_mutations)) %>%
mutate(QRDR_0_1_2_3 = if_else(QRDR_mutations>3, 3, QRDR_mutations)) %>%
mutate(QRDR_1_2 = if_else(QRDR_0_1_2==0, NA, QRDR_0_1_2)) %>%
mutate(QRDR_2_3 = if_else(QRDR_0_1_2<2, NA, QRDR_0_1_2_3))
# median DD zones, grouped by QRDR count
summary(as.numeric(QRDR_DD_noGenes_dat$Measurement)[QRDR_DD_noGenes_dat$QRDR_mutations==0])
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 6.00 27.00 29.00 28.67 30.00 44.00
summary(as.numeric(QRDR_DD_noGenes_dat$Measurement)[QRDR_DD_noGenes_dat$QRDR_mutations==1])
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 11.00 17.00 20.50 19.75 22.25 29.00
summary(as.numeric(QRDR_DD_noGenes_dat$Measurement)[QRDR_DD_noGenes_dat$QRDR_mutations>2])
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 6.000 6.000 6.000 6.676 6.000 12.000
# test for difference in DD zone with QRDR count
summary(lm(log2(as.numeric(Measurement)) ~ factor(QRDR_0_1_2), data=QRDR_DD_noGenes_dat))
##
## Call:
## lm(formula = log2(as.numeric(Measurement)) ~ factor(QRDR_0_1_2),
## data = QRDR_DD_noGenes_dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.24805 -0.07812 0.02497 0.07388 1.96617
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.833011 0.003863 1251.15 <2e-16 ***
## factor(QRDR_0_1_2)1 -0.564658 0.030848 -18.30 <2e-16 ***
## factor(QRDR_0_1_2)2 -1.892292 0.020766 -91.12 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1936 on 2638 degrees of freedom
## Multiple R-squared: 0.7645, Adjusted R-squared: 0.7644
## F-statistic: 4283 on 2 and 2638 DF, p-value: < 2.2e-16
summary(lm(log2(as.numeric(Measurement)) ~ QRDR_mutations, data=QRDR_DD_noGenes_dat))
##
## Call:
## lm(formula = log2(as.numeric(Measurement)) ~ QRDR_mutations,
## data = QRDR_DD_noGenes_dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.24831 -0.07839 0.02471 0.07362 1.57431
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.833275 0.003884 1244.46 <2e-16 ***
## QRDR_mutations -0.750349 0.008203 -91.47 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1953 on 2639 degrees of freedom
## Multiple R-squared: 0.7602, Adjusted R-squared: 0.7601
## F-statistic: 8367 on 1 and 2639 DF, p-value: < 2.2e-16
Fig1 - number of QRDR vs MIC, facet aac6 yes/no (no other
genes)
#MIC distribution for # QRDR, in absence of genes
QRDR_MIC_noGenes <- cipro_antibiogram %>%
filter(acquired_genes==0) %>%
filter(grepl('MIC.*$', Laboratory.Typing.Method)) %>%
mutate(aac6_label=if_else(aac6==0, "aac(6')-Ib-cr absent", "aac(6')-Ib-cr present")) %>%
mutate(QRDR_mutations=if_else(QRDR_mutations==4, 3, QRDR_mutations)) %>% # single isolate with 4 QRDR
ggplot(aes(x=factor(QRDR_mutations), y=as.numeric(Measurement))) +
geom_violin() +
geom_count(aes(colour = SRnwt)) +
geom_hline(aes(yintercept = 1), linetype = 2, alpha = 0.6, color = "black") +
geom_hline(aes(yintercept = 0.25), linetype = 1, alpha = 0.6, color = "black") +
facet_wrap(vars(aac6_label)) +
scale_y_continuous(trans = log2_trans(), breaks = 2^(-5:9), labels = function(x) round(as.numeric(x), digits = 3)) +
scale_color_manual(values = res_colours) +
theme_light() +
labs(y="MIC (mg/L)", x="", col="Phenotype",
title="No. QRDR mutations vs phenotype",
subtitle="(in absence of PMQR genes)")
QRDR_MIC_noGenes

QRDR_pheno_noGenes <- cipro_antibiogram %>%
filter(acquired_genes==0) %>%
filter(grepl('MIC.*$', Laboratory.Typing.Method)) %>%
mutate(aac6_label=if_else(aac6==0, "aac(6')-Ib-cr absent", "aac(6')-Ib-cr present")) %>%
mutate(QRDR_mutations=if_else(QRDR_mutations==4, 3, QRDR_mutations)) %>% # single isolate with 4 QRDR
ggplot(aes(x=factor(QRDR_mutations), fill=SRnwt)) +
geom_bar(stat='count', position='fill') +
facet_wrap(vars(aac6_label)) +
scale_fill_manual(values = res_colours) +
scale_y_continuous(labels=scales::percent_format()) +
geom_text(aes(label=..count..), stat="count", position=position_fill(vjust = .5), size=2) +
theme_light() +
labs(y="% Phenotype", x="Number of QRDR mutations") +
theme(legend.position="none", strip.background = element_blank(), strip.text = element_blank())
QRDR_MIC_noGenes / QRDR_pheno_noGenes + plot_layout(heights=c(3,1))

ggsave("figs/Fig1_numQRDR_MIC_pheno.pdf", width=6, height=5)
ggsave("figs/Fig1_numQRDR_MIC_pheno.png", width=6, height=5)
numbers for text - paragraph 5
# effect of qnr/qep genes, in absence of QRDR and aac6
# MIC data in absence of QRDR and aac6
qnr_MIC_nullBG_dat <- cipro_antibiogram %>%
filter(Laboratory.Typing.Method !="Disk diffusion") %>%
filter(aac6==0 & QRDR_mutations==0) %>%
select(Measurement, Resistance.phenotype, resistant, nonWT_binary, acquired_genes) %>%
mutate(acquired_bin=if_else(acquired_genes==0, 0, 1))
# MIC vs presence/absence of qnr, in absence of QRDR and aac6
summary(as.numeric(qnr_MIC_nullBG_dat$Measurement)[qnr_MIC_nullBG_dat$acquired_bin==1])
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.125 0.500 1.000 1.577 2.000 8.000
summary(as.numeric(qnr_MIC_nullBG_dat$Measurement)[qnr_MIC_nullBG_dat$acquired_bin==0])
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0300 0.2500 0.2500 0.3464 0.2500 64.0000
wilcox.test(log2(as.numeric(Measurement)) ~ acquired_bin, data=qnr_MIC_nullBG_dat)
##
## Wilcoxon rank sum test with continuity correction
##
## data: log2(as.numeric(Measurement)) by acquired_bin
## W = 144012, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0
summary(lm(log2(as.numeric(Measurement)) ~ acquired_bin, data=qnr_MIC_nullBG_dat))
##
## Call:
## lm(formula = log2(as.numeric(Measurement)) ~ acquired_bin, data = qnr_MIC_nullBG_dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.1877 -0.0558 -0.0558 -0.0558 7.9442
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.94422 0.01627 -119.50 <2e-16 ***
## acquired_bin 2.13191 0.03977 53.61 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9137 on 3786 degrees of freedom
## Multiple R-squared: 0.4315, Adjusted R-squared: 0.4314
## F-statistic: 2874 on 1 and 3786 DF, p-value: < 2.2e-16
# DD data in absence of QRDR and aac6
qnr_DD_noGenes_dat <- cipro_antibiogram %>%
filter(aac6==0 & QRDR_mutations==0) %>%
filter(Laboratory.Typing.Method=="Disk diffusion") %>%
select(Measurement, Resistance.phenotype, resistant, nonWT_binary, acquired_genes) %>%
mutate(acquired_bin=if_else(acquired_genes==0, 0, 1))
# DD vs presence/absence of qnr, in absence of QRDR and aac6
summary(as.numeric(qnr_DD_noGenes_dat$Measurement)[qnr_DD_noGenes_dat$acquired_bin==1])
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 6.00 17.00 21.00 19.51 22.00 31.00
summary(as.numeric(qnr_DD_noGenes_dat$Measurement)[qnr_DD_noGenes_dat$acquired_bin==0])
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 6.00 27.00 29.00 28.67 30.00 44.00
wilcox.test(as.numeric(Measurement) ~ acquired_bin, data=qnr_DD_noGenes_dat)
##
## Wilcoxon rank sum test with continuity correction
##
## data: as.numeric(Measurement) by acquired_bin
## W = 230345, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0
summary(lm(as.numeric(Measurement) ~ acquired_bin, data=qnr_DD_noGenes_dat))
##
## Call:
## lm(formula = as.numeric(Measurement) ~ acquired_bin, data = qnr_DD_noGenes_dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -22.6659 -1.6659 0.3341 1.3341 15.3341
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 28.66587 0.05979 479.41 <2e-16 ***
## acquired_bin -9.15545 0.31160 -29.38 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.996 on 2605 degrees of freedom
## Multiple R-squared: 0.2489, Adjusted R-squared: 0.2486
## F-statistic: 863.3 on 1 and 2605 DF, p-value: < 2.2e-16
# all genomes with no acquired genes (MIC/DD)
qnr_noGenes_dat <- cipro_antibiogram %>%
filter(aac6==0 & QRDR_mutations==0) %>%
select(Measurement, Resistance.phenotype, resistant, nonWT_binary, acquired_genes) %>%
mutate(acquired_bin=if_else(acquired_genes==0, 0, 1))
dim(qnr_noGenes_dat)
## [1] 6395 6
# NWT vs presence/absence of qnr, in absence of QRDR and aac6
fisher.test(table(qnr_noGenes_dat$nonWT_binary, qnr_noGenes_dat$acquired_bin))
##
## Fisher's Exact Test for Count Data
##
## data: table(qnr_noGenes_dat$nonWT_binary, qnr_noGenes_dat$acquired_bin)
## p-value < 2.2e-16
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 84.12867 150.39683
## sample estimates:
## odds ratio
## 112.2407
# resistance vs presence/absence of qnr, in absence of QRDR and aac6
fisher.test(table(qnr_noGenes_dat$resistant, qnr_noGenes_dat$acquired_bin))
##
## Fisher's Exact Test for Count Data
##
## data: table(qnr_noGenes_dat$resistant, qnr_noGenes_dat$acquired_bin)
## p-value < 2.2e-16
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 68.34106 108.80715
## sample estimates:
## odds ratio
## 86.07533
# MIC vs qnr count
summary(lm(log2(as.numeric(Measurement)) ~ acquired_genes, data=qnr_MIC_nullBG_dat))
##
## Call:
## lm(formula = log2(as.numeric(Measurement)) ~ acquired_genes,
## data = qnr_MIC_nullBG_dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.1214 -0.0626 -0.0626 -0.0626 7.9374
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.93745 0.01625 -119.2 <2e-16 ***
## acquired_genes 2.02751 0.03797 53.4 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9152 on 3786 degrees of freedom
## Multiple R-squared: 0.4296, Adjusted R-squared: 0.4295
## F-statistic: 2852 on 1 and 3786 DF, p-value: < 2.2e-16
summary(as.numeric(qnr_MIC_nullBG_dat$Measurement)[qnr_MIC_nullBG_dat$acquired_genes==1])
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.125 0.500 1.000 1.548 2.000 8.000
summary(as.numeric(qnr_MIC_nullBG_dat$Measurement)[qnr_MIC_nullBG_dat$acquired_genes==2])
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.500 1.000 2.000 2.475 4.000 4.000
summary(as.numeric(qnr_MIC_nullBG_dat$Measurement)[qnr_MIC_nullBG_dat$acquired_genes>2])
## Min. 1st Qu. Median Mean 3rd Qu. Max.
##
# DD vs qnr count
summary(lm(as.numeric(Measurement) ~ acquired_genes, data=qnr_DD_noGenes_dat))
##
## Call:
## lm(formula = as.numeric(Measurement) ~ acquired_genes, data = qnr_DD_noGenes_dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -22.6642 -1.6642 0.3358 1.3358 15.3358
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 28.66417 0.05939 482.65 <2e-16 ***
## acquired_genes -8.65839 0.28782 -30.08 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.978 on 2605 degrees of freedom
## Multiple R-squared: 0.2578, Adjusted R-squared: 0.2575
## F-statistic: 905 on 1 and 2605 DF, p-value: < 2.2e-16
summary(as.numeric(qnr_DD_noGenes_dat$Measurement)[qnr_DD_noGenes_dat$acquired_genes==1])
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 6.00 18.00 21.00 19.91 22.50 31.00
summary(as.numeric(qnr_DD_noGenes_dat$Measurement)[qnr_DD_noGenes_dat$acquired_genes==2])
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 6.0 6.0 10.0 12.2 17.0 22.0
summary(as.numeric(qnr_DD_noGenes_dat$Measurement)[qnr_DD_noGenes_dat$acquired_genes>2])
## Min. 1st Qu. Median Mean 3rd Qu. Max.
##
numbers for text - paragraph 6, effect of aac6 gene, in absence of
QRDR and other acquired
# MIC
aac_MIC_nullBG_dat <- cipro_antibiogram %>%
filter(Laboratory.Typing.Method !="Disk diffusion") %>%
filter(acquired_genes==0 & QRDR_mutations==0) %>%
select(Measurement, Resistance.phenotype, resistant, nonWT_binary, aac6)
# MIC vs presence/absence of qnr
wilcox.test(log2(as.numeric(Measurement)) ~ aac6, data=aac_MIC_nullBG_dat)
##
## Wilcoxon rank sum test with continuity correction
##
## data: log2(as.numeric(Measurement)) by aac6
## W = 176904, p-value = 1.255e-12
## alternative hypothesis: true location shift is not equal to 0
summary(lm(log2(as.numeric(Measurement)) ~ aac6, data=aac_MIC_nullBG_dat))
##
## Call:
## lm(formula = log2(as.numeric(Measurement)) ~ aac6, data = aac_MIC_nullBG_dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.1147 -0.0558 -0.0558 -0.0558 7.9442
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.94422 0.01539 -126.347 < 2e-16 ***
## aac6 0.58531 0.07268 8.053 1.12e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8642 on 3300 degrees of freedom
## Multiple R-squared: 0.01927, Adjusted R-squared: 0.01898
## F-statistic: 64.85 on 1 and 3300 DF, p-value: 1.119e-15
summary(as.numeric(aac_MIC_nullBG_dat$Measurement)[aac_MIC_nullBG_dat$aac6==1])
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.1200 0.2500 0.2500 0.6021 0.5000 4.0000
summary(as.numeric(aac_MIC_nullBG_dat$Measurement)[aac_MIC_nullBG_dat$aac6==0])
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0300 0.2500 0.2500 0.3464 0.2500 64.0000
table(aac_MIC_nullBG_dat$aac6, aac_MIC_nullBG_dat$Resistance.phenotype)/rowSums(table(aac_MIC_nullBG_dat$aac6, aac_MIC_nullBG_dat$Resistance.phenotype))
##
## I R S
## 0 0.08497146 0.03899810 0.87603044
## 1 0.15540541 0.16891892 0.67567568
# DD
aac_DD_nullBG_dat <- cipro_antibiogram %>%
filter(acquired_genes==0 & QRDR_mutations==0) %>%
filter(Laboratory.Typing.Method=="Disk diffusion") %>%
select(Measurement, Resistance.phenotype, resistant, nonWT_binary, aac6)
wilcox.test(as.numeric(Measurement) ~ aac6, data=aac_DD_nullBG_dat)
##
## Wilcoxon rank sum test with continuity correction
##
## data: as.numeric(Measurement) by aac6
## W = 25156, p-value = 5.356e-05
## alternative hypothesis: true location shift is not equal to 0
summary(lm(as.numeric(Measurement) ~ aac6, data=aac_DD_nullBG_dat))
##
## Call:
## lm(formula = as.numeric(Measurement) ~ aac6, data = aac_DD_nullBG_dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -22.6659 -1.6659 0.3341 1.3341 15.3341
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 28.66587 0.05766 497.182 < 2e-16 ***
## aac6 -5.08254 0.83602 -6.079 1.39e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.889 on 2521 degrees of freedom
## Multiple R-squared: 0.01445, Adjusted R-squared: 0.01406
## F-statistic: 36.96 on 1 and 2521 DF, p-value: 1.389e-09
summary(as.numeric(aac_DD_nullBG_dat$Measurement)[aac_DD_nullBG_dat$aac6==1])
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 16.00 21.00 23.50 23.58 27.25 29.00
summary(as.numeric(aac_DD_nullBG_dat$Measurement)[aac_DD_nullBG_dat$aac6==0])
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 6.00 27.00 29.00 28.67 30.00 44.00
table(aac_DD_nullBG_dat$aac6, aac_DD_nullBG_dat$Resistance.phenotype)/rowSums(table(aac_DD_nullBG_dat$aac6, aac_DD_nullBG_dat$Resistance.phenotype))
##
## I R S
## 0 0.04022302 0.01194743 0.94782955
## 1 0.25000000 0.33333333 0.41666667
# all genomes with no acquired genes (MIC/DD)
qnr_noGenes_dat <- cipro_antibiogram %>%
filter(acquired_genes==0 & QRDR_mutations==0) %>%
select(Measurement, Resistance.phenotype, resistant, nonWT_binary, aac6)
dim(qnr_noGenes_dat)
## [1] 5825 5